In the field of Magnetic Resonance Imaging (MRI), intensity inhomogeneities are generally attributed to inhomogeneities in the static field of the magnetic resonance system. For purposes of nomenclature used herein, the phrase intensity inhomogeneity may also be referred to as intensity nonuniformity, bias field, gain field, or shading. This bias field can also be caused by inhomogeneities in the periodic RF pulse. The presence of intensity inhomogeneities within images are also influenced by the distance of the tissue to the coil measuring the signal and of the subject/object being imaged. The resulting effect is sometimes referred to as a bias field, gain field, shading, intensity nonuniformity or intensity inhomogeneity. In particular, the effect is that a given type of tissue will have different intensities depending on where in the image the tissue is located.
Bias fields are problematic because they can adversely impact image quality, for example they can greatly reduce contrast across an image. This can negatively impact clinical decisions being made based on these images. Human vision can to some extent compensate for these effects, but algorithms can do this less well. Bias fields also adversely affect quantitative image analysis, such as segmentation and registration algorithms. The same tissue in different parts of the image can have significantly different image intensities. Bias fields may obscure regions of interest because they are not all visible at a single window/level view. The problems described above are even more pronounced at 3 tesla (T) and higher fields, which are increasingly used field levels.
Reference is made to FIG. 5, which illustrates an example of a homogenous body in a magnetic resonance system in which the corresponding MR image contains intensity inhomogeneities. Ideally, the intensities should be identical throughout the body 600. That is, the color and/or intensity seen throughout the scanned body should be uniform. However, as seen in FIG. 5, the top 602 and bottom 604 regions of the body shown (also referred to as a “phantom”) have a higher intensity level, as illustrated by brighter regions. The presence of the intensity inhomogeneity across the phantom is due in part to the fact that this portion of the imaged body is physically closer to the measuring coils used to receive signals.
Conventional approaches to removing intensity inhomogeneities include estimating the bias field through iterative algorithms. However, one perceived shortcoming with this approach is that this approach can be relatively slow. More important perceived shortcomings are the fundamental assumptions built into such algorithms. These approaches often have implicit assumptions about the shape/smoothness of the bias field and do not mobilize the full amount of available information. As such, their performance may be prone to the initialization of the algorithm. Other approaches for addressing intensity inhomogeneity in MR images are discussed in “A Review of Methods for Correction of Intensity Inhomogeneity in MRI” (Vovk et al.; IEEE Transactions on Medical Imaging, Vo. 26, No. 3, March 2007; p. 405-421; B. Belaroussi et al, Med Image Anal, vol. 10, pp. 234-46, 2006; Z. Hou, International Journal of Biomedical Imaging, vol. 2006, ID 49515, doi: 10.1155/IJBI/2006/49515, pp. 11, 2006, and the references therein). These approaches, however, appear to be computationally intensive in nature.
Another known approach to correcting the bias field is based on calibration scans using phantoms. However, one perceived shortcoming with this technique is that this method is generally inappropriate since the bias field depends on the loading of the machine by the tissue of the specific patient. That is, the bias field is very specific to the patient. Generally estimating the bias field from a homogenous phantom will therefore create erroneous results. Another approach involves using calibration scans using different coils. The bias field also depends on the coil which is used to image the patient. As such, using one coil to estimate the bias field while using another coil for the actual imaging process will yield an inaccurate estimate.
Other approaches include physics/pulse sequence based approaches. One such approach involves using proton density weighted images as calibration images. Because the value of the repetition time (TR) of proton density weighted images is very large, it takes a very long time to acquire such images. Stollberger et al. (Magn Reson Med, vol. 35, pp. 246-51, 1996) teaches of a method which uses the signal intensity ratio of two images measured with different excitation angles (alpha and 2*alpha) and a repetition time TR>=5 T1. With this method, the active bias field can be determined in vivo in 23 cross-sections in less than 6 min. The imaging time is proportional to TR, and therefore, having a long TR (>=5 T1) increases the scan time. TR in this instance is about 2000 msec. Longitudinal relaxation time T1 is a tissue specific constant which only depends on the external field. For 3T fields in new MR systems, the T1 increases, which means that this method would take even longer to run. A similar approach (H. Mihara et al Magma, vol. 7, pp. 115-20, 1998) to the one taught by Stollberger et al. uses a TR of 1600 msec. TR must be long enough to minimize the effect of T1 relaxation. As such, this means TR>(3 to 5)*T1. Thus, at 1.5 T: T1 (liver)˜600 msec; T1 (spleen)˜1000 msec; T1 (fat)˜350 msec; T1 (brain) between 600 msec and 1000 msec.
Inhomogeneities in MR images can also occur in the radio frequency (RF) transmit field B1+.
Accordingly, various needs exist in the industry to address the aforementioned deficiencies and inadequacies.